Добірка наукової літератури з теми "DYNAMIC MACHINE LEARNING METHODOLOGY"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "DYNAMIC MACHINE LEARNING METHODOLOGY".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "DYNAMIC MACHINE LEARNING METHODOLOGY"
1
Barr, Joseph R., Eden A. Ellis, Antonio Kassab, Christian L. Redfearn, Narayanan Nani Srinivasan, and Kurtis B. Voris. "Home Price Index: A Machine Learning Methodology." International Journal of Semantic Computing 11, no. 01 (March 2017): 111–33. http://dx.doi.org/10.1142/s1793351x17500015.
Повний текст джерелаАнотація:
Estimating house prices is essential for homeowners and investors alike with both needing to understand the value of their asset, and to understand real estate assets as part of an overall portfolios. Commonly-used indices like the National Association of Realtors (NAR) median home price index, or the celebrated Case-Shiller Home Price Index are reported exclusively over a large geographic areas, i.e., a metropolitan, whereby home price dynamics are lost. In this paper, we propose a improved method to capture price dynamics over time at the most granular level possible — a single home. Using over 16 years of home sale data, from the year 2000 to 2016, we estimate home price index for each house. Once home price dynamics is captured, its possible to aggregate price dynamics to construct a price index over geographies of any kind, e.g., ZIP code. This particular index relies on a so-called ‘gradient boosted’ model, a methodology framework relying on multiple calibration parameters and heavily dependent on sampling techniques. We demonstrate that this approach offers several strengths compared to the commonly reported indices, the ‘median sale’ and ‘repeat sales’ indices.
2
Pérez Moreno, F., V. F. Gómez Comendador, R. Delgado-Aguilera Jurado, M. Zamarreño Suárez, D. Janisch, and R. M. Arnaldo Valdés. "Dynamic sector characterisation model with the application of machine learning techniques." IOP Conference Series: Materials Science and Engineering 1226, no. 1 (February 1, 2022): 012018. http://dx.doi.org/10.1088/1757-899x/1226/1/012018.
Повний текст джерелаАнотація:
Abstract The ATC service has the objective of controlling airspace operations safely and efficiently. This control is becoming more and more difficult due to the increasing complexity of airspace. With the objective of collaborating and facilitating the provision of the control service, FLUJOS project aims to develop a methodology to characterise ATC sectors according to their complexity. This paper shows the first results obtained in this project. A methodology is proposed that first performs a statistical analysis of the data present in the flight plans of individual aircraft. The statistical analysis will be used to estimate the impact of air traffic flows. With this, the complexity of ATC sectors will finally be determined. In addition, a machine learning tool will be added to develop a dynamic methodology. After evaluating the methodology with data from Spanish airspace in 2019, it can be said that the results obtained are logical from an operational point of view, and that they allow a fairly accurate classification of the sectors based on their complexity. However, the proposed methodology is still a preliminary version, so more work will have to be done to add variables to achieve the objective of obtaining an even more accurate and realistic classification.
3
Navarro, Osvaldo, Jones Yudi, Javier Hoffmann, Hector Gerardo Muñoz Hernandez, and Michael Hübner. "A Machine Learning Methodology for Cache Memory Design Based on Dynamic Instructions." ACM Transactions on Embedded Computing Systems 19, no. 2 (March 17, 2020): 1–20. http://dx.doi.org/10.1145/3376920.
Повний текст джерела
4
PRIORE, PAOLO, DAVID DE LA FUENTE, ALBERTO GOMEZ, and JAVIER PUENTE. "DYNAMIC SCHEDULING OF MANUFACTURING SYSTEMS WITH MACHINE LEARNING." International Journal of Foundations of Computer Science 12, no. 06 (December 2001): 751–62. http://dx.doi.org/10.1142/s0129054101000849.
Повний текст джерелаАнотація:
A common way of scheduling jobs dynamically in a manufacturing system is by means of dispatching rules. The drawback of this method is that the performance of these rules depends on the state the system is in at each moment, and no one rule exists that overrules the rest in all the possible states that the system may be in. It would therefore be interesting to use the most appropriate rule at each moment. To achieve this goal, a scheduling approach which uses machine learning is presented in this paper. The methodology proposed in this paper may be divided into five basic steps. Firstly, definition of the appropriate control attributes for identifying the relevant manufacturing patterns. In second place, creation of a set of training examples using different values of the control attributes. Subsequently, acquiring of heuristic rules by means of a machine learning program. Then, using of the previously calculated heuristic rules to select the most appropriate dispatching rules, and finally testing of the performance of the approach. The approach that we propose is applied to a flow shop system and to a classic job shop configuration. The results demonstrate that this approach produces an improvement in the performance of the system when compared to the traditional method of using dispatching rules.
5
Iskhakov, Fedor, John Rust, and Bertel Schjerning. "Machine learning and structural econometrics: contrasts and synergies." Econometrics Journal 23, no. 3 (August 29, 2020): S81—S124. http://dx.doi.org/10.1093/ectj/utaa019.
Повний текст джерелаАнотація:
Summary We contrast machine learning (ML) and structural econometrics (SE), focusing on areas where ML can advance the goals of SE. Our views have been informed and inspired by the contributions to this special issue and by papers presented at the second conference on dynamic structural econometrics at the University of Copenhagen in 2018, ‘Methodology and Applications of Structural Dynamic Models and Machine Learning'. ML offers a promising class of techniques that can significantly extend the set of questions we can analyse in SE. The scope, relevance and impact of empirical work in SE can be improved by following the lead of ML in questioning and relaxing the assumption of unbounded rationality. For the foreseeable future, however, ML is unlikely to replace the essential role of human creativity and knowledge in model building and inference, particularly with respect to the key goal of SE, counterfactual prediction.
6
Ko, Jeong Hoon. "Machining Stability Categorization and Prediction Using Process Model Guided Machine Learning." Metals 12, no. 2 (February 9, 2022): 298. http://dx.doi.org/10.3390/met12020298.
Повний текст джерелаАнотація:
The time-domain dynamic process model is used to generate data and guides the stability criteria for machine learning, saving the experimental costs for a number of required data for the metal process. Fourier transformation of vibration data simulated using a dynamic process model generates the feature lists including multiple frequencies and amplitudes at each process condition. The feature lists for milling stability are analyzed for training the machine learning algorithm. The amplitude and frequency distributions may change according to the dynamic pattern of the machining stability. The vibration patterns are grouped into stable, chatter, and boundary conditions by performing data training using support vector machines and gradient tree boosting. In the high-speed milling of Al6061-T6 with 6000 to 18,000 RPM and variations of axial and radial depths of cuts, 2400 data sets of the time domain data were trained and tested. Actual experimental tests are carried out for new process conditions with the range of 9890 to 28,470 RPM and 989 to 2847 mm/min. The experimental stability outcomes are compared with predictions from the algorithms. Stability is accurately predicted over new conditions with around 0.9 prediction accuracy, which means the methodology can be used to predict, categorize, and monitor stability in end milling processes.
7
García Plaza, Eustaquio, Pedro Jose Núñez López, Angel Ramon Martín, and E. Beamud. "Virtual Machining Applied to the Teaching of Manufacturing Technology." Materials Science Forum 692 (July 2011): 120–27. http://dx.doi.org/10.4028/www.scientific.net/msf.692.120.
Повний текст джерелаАнотація:
Teaching methodology for industrial engineering must adapt and update its pedagogy by adopting innovative and dynamic approaches to training in state-of-the-art manufacturing technology. The development of virtual reality and computer simulation software has significantly improved the quality of education by raising learner motivation, commitment, and participation in the learning process. In university contexts characterised by large numbers of students, a hands-on approach to training in machine-tool operation on lathes and mills is unfeasible. Hence, the teaching methodology proposed involves the use of machine-tool simulators to undertake practical tasks in a virtual learning environment. The learning tasks focus on the main machine-tool components and their movements as well as on the principles and operations of machining in turning and milling processes performed on virtual machine where learners can acquire skills similar to those using traditional methodology, but require fewer resources and learning time spans.
8
Hewawasam, Hasitha, Gayan Kahandawa, and Yousef Ibrahim. "Machine Learning-Based Agoraphilic Navigation Algorithm for Use in Dynamic Environments with a Moving Goal." Machines 11, no. 5 (April 28, 2023): 513. http://dx.doi.org/10.3390/machines11050513.
Повний текст джерелаАнотація:
This paper presents a novel development of a new machine learning-based control system for the Agoraphilic (free-space attraction) concept of navigating robots in unknown dynamic environments with a moving goal. Furthermore, this paper presents a new methodology to generate training and testing datasets to develop a machine learning-based module to improve the performances of Agoraphilic algorithms. The new algorithm presented in this paper utilises the free-space attraction (Agoraphilic) concept to safely navigate a mobile robot in a dynamically cluttered environment with a moving goal. The algorithm uses tracking and prediction strategies to estimate the position and velocity vectors of detected moving obstacles and the goal. This predictive methodology enables the algorithm to identify and incorporate potential future growing free-space passages towards the moving goal. This is supported by the new machine learning-based controller designed specifically to efficiently account for the high uncertainties inherent in the robot’s operational environment with a moving goal at a reduced computational cost. This paper also includes comparative and experimental results to demonstrate the improvements of the algorithm after introducing the machine learning technique. The presented experiments demonstrated the success of the algorithm in navigating robots in dynamic environments with the challenge of a moving goal.
9
Lu, M., L. Groeneveld, D. Karssenberg, S. Ji, R. Jentink, E. Paree, and E. Addink. "GEOMORPHOLOGICAL MAPPING OF INTERTIDAL AREAS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 28, 2021): 75–80. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-75-2021.
Повний текст джерелаАнотація:
Abstract. Spatiotemporal geomorphological mapping of intertidal areas is essential for understanding system dynamics and provides information for ecological conservation and management. Mapping the geomorphology of intertidal areas is very challenging mainly because spectral differences are oftentimes relatively small while transitions between geomorphological units are oftentimes gradual. Also, the intertidal areas are highly dynamic. Considerable challenges are to distinguish between different types of tidal flats, specifically, low and high dynamic shoal flats, sandy and silty low dynamic flats, and mega-ripple areas. In this study, we harness machine learning methods and compare between machine learning methods using features calculated in classical Object-Based Image Analysis (OBIA) vs. end-to-end deep convolutional neural networks that derive features directly from imagery, in automated geomorphological mapping. This study expects to gain us an in-depth understanding of features that contribute to tidal area classification and greatly improve the automation and prediction accuracy. We emphasise model interpretability and knowledge mining. By comparing and combing object-based and deep learning-based models, this study contributes to the development and integration of both methodology domains for semantic segmentation.
10
Carputo, Francesco, Danilo D’Andrea, Giacomo Risitano, Aleksandr Sakhnevych, Dario Santonocito, and Flavio Farroni. "A Neural-Network-Based Methodology for the Evaluation of the Center of Gravity of a Motorcycle Rider." Vehicles 3, no. 3 (July 15, 2021): 377–89. http://dx.doi.org/10.3390/vehicles3030023.
Повний текст джерелаАнотація:
A correct reproduction of a motorcycle rider’s movements during driving is a crucial and the most influential aspect of the entire motorcycle–rider system. The rider performs significant variations in terms of body configuration on the vehicle in order to optimize the management of the motorcycle in all the possible dynamic conditions, comprising cornering and braking phases. The aim of the work is to focus on the development of a technique to estimate the body configurations of a high-performance driver in completely different situations, starting from the publicly available videos, collecting them by means of image acquisition methods, and employing machine learning and deep learning techniques. The technique allows us to determine the calculation of the center of gravity (CoG) of the driver’s body in the video acquired and therefore the CoG of the entire driver–vehicle system, correlating it to commonly available vehicle dynamics data, so that the force distribution can be properly determined. As an additional feature, a specific function correlating the relative displacement of the driver’s CoG towards the vehicle body and the vehicle roll angle has been determined starting from the data acquired and processed with the machine and the deep learning techniques.
Дисертації з теми "DYNAMIC MACHINE LEARNING METHODOLOGY"
1
Early, Kirstin. "Dynamic Question Ordering: Obtaining Useful Information While Reducing User Burden." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1117.
Повний текст джерелаАнотація:
As data become more pervasive and computing power increases, the opportunity for transformative use of data grows. Collecting data from individuals can be useful to the individuals (by providing them with personalized predictions) and the data collectors (by providing them with information about populations). However, collecting these data is costly: answering survey items, collecting sensed data, and computing values of interest deplete finite resources of time, battery, life, money, etc. Dynamically ordering the items to be collected, based on already known information (such as previously collected items or paradata), can lower the costs of data collection by tailoring the information-acquisition process to the individual. This thesis presents a framework for an iterative dynamic item ordering process that trades off item utility with item cost at data collection time. The exact metrics for utility and cost are application-dependent, and this frame- work can apply to many domains. The two main scenarios we consider are (1) data collection for personalized predictions and (2) data collection in surveys. We illustrate applications of this framework to multiple problems ranging from personalized prediction to questionnaire scoring to government survey collection. We compare data quality and acquisition costs of our method to fixed order approaches and show that our adaptive process obtains results of similar quality at lower cost. For the personalized prediction setting, the goal of data collection is to make a prediction based on information provided by a respondent. Since it is possible to give a reasonable prediction with only a subset of items, we are not concerned with collecting all items. Instead, we want to order the items so that the user provides information that most increases the prediction quality, while not being too costly to provide. One metric for quality is prediction certainty, which reflects how likely the true value is to coincide with the estimated value. Depending whether the prediction problem is continuous or discrete, we use prediction interval width or predicted class probability to measure the certainty of a prediction. We illustrate the results of our dynamic item ordering framework on tasks of predicting energy costs, student stress levels, and device identification in photographs and show that our adaptive process achieves equivalent error rates as a fixed order baseline with cost savings up to 45%. For the survey setting, the goal of data collection is often to gather information from a population, and it is desired to have complete responses from all samples. In this case, we want to maximize survey completion (and the quality of necessary imputations), and so we focus on ordering items to engage the respondent and collect hopefully all the information we seek, or at least the information that most characterizes the respondent so imputed values will be accurate. One item utility metric for this problem is information gain to get a “representative” set of answers from the respondent. Furthermore, paradata collected during the survey process can inform models of user engagement that can influence either the utility metric ( e.g., likelihood therespondent will continue answering questions) or the cost metric (e.g., likelihood the respondent will break off from the survey). We illustrate the benefit of dynamic item ordering for surveys on two nationwide surveys conducted by the U.S. Census Bureau: the American Community Survey and the Survey of Income and Program Participation.
2
Zhang, Bo. "Machine Learning on Statistical Manifold." Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/hmc_theses/110.
Повний текст джерелаАнотація:
This senior thesis project explores and generalizes some fundamental machine learning algorithms from the Euclidean space to the statistical manifold, an abstract space in which each point is a probability distribution. In this thesis, we adapt the optimal separating hyperplane, the k-means clustering method, and the hierarchical clustering method for classifying and clustering probability distributions. In these modifications, we use the statistical distances as a measure of the dissimilarity between objects. We describe a situation where the clustering of probability distributions is needed and useful. We present many interesting and promising empirical clustering results, which demonstrate the statistical-distance-based clustering algorithms often outperform the same algorithms with the Euclidean distance in many complex scenarios. In particular, we apply our statistical-distance-based hierarchical and k-means clustering algorithms to the univariate normal distributions with k = 2 and k = 3 clusters, the bivariate normal distributions with diagonal covariance matrix and k = 3 clusters, and the discrete Poisson distributions with k = 3 clusters. Finally, we prove the k-means clustering algorithm applied on the discrete distributions with the Hellinger distance converges not only to the partial optimal solution but also to the local minimum.
3
Höstklint, Niklas, and Jesper Larsson. "Dynamic Test Case Selection using Machine Learning." Thesis, KTH, Hälsoinformatik och logistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-296634.
Повний текст джерелаАнотація:
Testing code is a vital part at any software producing company, to ensure no faulty code slips through that could have detrimental consequences. At Ericsson, testing code before publishing is a very costly process which can take several hours. Currently, every single test is run for all submitted code. This report aims to address the issue by building a machine learning model that determines which tests need to be run, so that unnecessary tests are left out, saving time and resources. It is however important to find the failures, as having certain failures pass through into production could have all types of economic, environmental and social consequences. The result shows that there is great potential in several different types of models. A Linear Regression model found 92% of all failures within running 25% of all test categories. The linear model however plateaus before finding the final failures. If finding 100% of failures is essential, a Support Vector Regression model proved the most efficient as it was the only model to find 100% of failures within 90% of test categories being run.Testning av kod är en avgörande del för alla mjukvaruproducerande företag, för att säkerställa att ingen felaktig kod som kan ha skadlig påverkan publiceras. Hos Ericsson är testning av kod innan det ska publiceras en väldigt dyr process som kan ta flera timmar. Vid tiden denna rapport skrivs så körs varenda test för all inlämnad kod. Denna rapport har som mål att lösa/reducera problemet genom att bygga en modell med maskininlärning som avgör vilka tester som ska köras, så onödiga tester lämnas utanför vilket i sin tur sparar tid och resurser. Dock är det viktigt att hitta alla misslyckade tester, eftersom att tillåta dessa passera till produktionen kan innebära alla möjliga olika ekonomiska, miljömässiga och sociala konsekvenser. Resultaten visar att det finns stor potential i flera olika typer av modeller. En linjär regressionsmodell hittade 92% av alla fel inom att 25% av alla test kategorier körts. Den linjära modellen träffar dock en platå innan den hittar de sista felen. Om det är essentiellt att hitta 100% av felen, så visade sig en support vector regressionsmodell vara mest effektiv, då den var den enda modellen som lyckades hitta 100% av alla fel inom att 90% alla test kategorier hade körts.
4
Rowe, Michael C. (Michael Charles). "A Machine Learning Method Suitable for Dynamic Domains." Thesis, University of North Texas, 1996. https://digital.library.unt.edu/ark:/67531/metadc278720/.
Повний текст джерелаАнотація:
The efficacy of a machine learning technique is domain dependent. Some machine learning techniques work very well for certain domains but are ill-suited for other domains. One area that is of real-world concern is the flexibility with which machine learning techniques can adapt to dynamic domains. Currently, there are no known reports of any system that can learn dynamic domains, short of starting over (i.e., re-running the program). Starting over is neither time nor cost efficient for real-world production environments. This dissertation studied a method, referred to as Experience Based Learning (EBL), that attempts to deal with conditions related to learning dynamic domains. EBL is an extension of Instance Based Learning methods. The hypothesis of the study related to this research was that the EBL method would automatically adjust to domain changes and still provide classification accuracy similar to methods that require starting over. To test this hypothesis, twelve widely studied machine learning datasets were used. A dynamic domain was simulated by presenting these datasets in an uninterrupted cycle of train, test, and retrain. The order of the twelve datasets and the order of records within each dataset were randomized to control for order biases in each of ten runs. As a result, these methods provided datasets that represent extreme levels of domain change. Using the above datasets, EBL's mean classification accuracies for each dataset were compared to the published static domain results of other machine learning systems. The results indicated that the EBL's system performance was not statistically different (p>0.30) from the other machine learning methods. These results indicate that the EBL system is able to adjust to an extreme level of domain change and yet produce satisfactory results. This finding supports the use of the EBL method in real-world environments that incur rapid changes to both variables and values.
5
Kelly, Michael A. "A methodology for software cost estimation using machine learning techniques." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from the National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA273158.
Повний текст джерелаАнотація:
Thesis (M.S. in Information Technology Management) Naval Postgraduate School, September 1993.Thesis advisor(s): Ramesh, B. ; Abdel-Hamid, Tarek K. "September 1993." Bibliography: p. 135. Also available online.
6
Narmack, Kirilll. "Dynamic Speed Adaptation for Curves using Machine Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233545.
Повний текст джерелаАнотація:
The vehicles of tomorrow will be more sophisticated, intelligent and safe than the vehicles of today. The future is leaning towards fully autonomous vehicles. This degree project provides a data driven solution for a speed adaptation system that can be used to compute a vehicle speed for curves, suitable for the underlying driving style of the driver, road properties and weather conditions. A speed adaptation system for curves aims to compute a vehicle speed suitable for curves that can be used in Advanced Driver Assistance Systems (ADAS) or in Autonomous Driving (AD) applications. This degree project was carried out at Volvo Car Corporation. Literature in the field of speed adaptation systems and factors affecting the vehicle speed in curves was reviewed. Naturalistic driving data was both collected by driving and extracted from Volvo's data base and further processed. A novel speed adaptation system for curves was invented, implemented and evaluated. This speed adaptation system is able to compute a vehicle speed suitable for the underlying driving style of the driver, road properties and weather conditions. Two different artificial neural networks and two mathematical models were used to compute the desired vehicle speed in curves. These methods were compared and evaluated.Morgondagens fordon kommer att vara mer sofistikerade, intelligenta och säkra än dagens fordon. Framtiden lutar mot fullständigt autonoma fordon. Detta examensarbete tillhandahåller en datadriven lösning för ett hastighetsanpassningssystem som kan beräkna ett fordons hastighet i kurvor som är lämpligt för förarens körstil, vägens egenskaper och rådande väder. Ett hastighetsanpassningssystem för kurvor har som mål att beräkna en fordonshastighet för kurvor som kan användas i Advanced Driver Assistance Systems (ADAS) eller Autonomous Driving (AD) applikationer. Detta examensarbete utfördes på Volvo Car Corporation. Litteratur kring hastighetsanpassningssystem samt faktorer som påverkar ett fordons hastighet i kurvor studerades. Naturalistisk bilkörningsdata samlades genom att köra bil samt extraherades från Volvos databas och bearbetades. Ett nytt hastighetsanpassningssystem uppfanns, implementerades samt utvärderades. Hastighetsanpassningssystemet visade sig vara kapabelt till att beräkna en lämplig fordonshastighet för förarens körstil under rådande väderförhållanden och vägens egenskaper. Två olika artificiella neuronnätverk samt två matematiska modeller användes för att beräkna fordonets hastighet. Dessa metoder jämfördes och utvärderades.
7
Sîrbu, Adela-Maria. "Dynamic machine learning for supervised and unsupervised classification." Thesis, Rouen, INSA, 2016. http://www.theses.fr/2016ISAM0002/document.
Повний текст джерелаАнотація:
La direction de recherche que nous abordons dans la thèse est l'application des modèles dynamiques d'apprentissage automatique pour résoudre les problèmes de classification supervisée et non supervisée. Les problèmes particuliers que nous avons décidé d'aborder dans la thèse sont la reconnaissance des piétons (un problème de classification supervisée) et le groupement des données d'expression génétique (un problème de classification non supervisée). Les problèmes abordés sont représentatifs pour les deux principaux types de classification et sont très difficiles, ayant une grande importance dans la vie réelle. La première direction de recherche que nous abordons dans le domaine de la classification non supervisée dynamique est le problème de la classification dynamique des données d'expression génétique. L'expression génétique représente le processus par lequel l'information d'un gène est convertie en produits de gènes fonctionnels : des protéines ou des ARN ayant différents rôles dans la vie d'une cellule. La technologie des micro-réseaux moderne est aujourd'hui utilisée pour détecter expérimentalement les niveaux d'expression de milliers de gènes, dans des conditions différentes et au fil du temps. Une fois que les données d'expression génétique ont été recueillies, l'étape suivante consiste à analyser et à extraire des informations biologiques utiles. L'un des algorithmes les plus populaires traitant de l'analyse des données d'expression génétique est le groupement, qui consiste à diviser un certain ensemble en groupes, où les composants de chaque groupe sont semblables les uns aux autres données. Dans le cas des ensembles de données d'expression génique, chaque gène est représenté par ses valeurs d'expression (caractéristiques), à des points distincts dans le temps, dans les conditions contrôlées. Le processus de regroupement des gènes est à la base des études génomiques qui visent à analyser les fonctions des gènes car il est supposé que les gènes qui sont similaires dans leurs niveaux d'expression sont également relativement similaires en termes de fonction biologique. Le problème que nous abordons dans le sens de la recherche de classification non supervisée dynamique est le regroupement dynamique des données d'expression génique. Dans notre cas, la dynamique à long terme indique que l'ensemble de données ne sont pas statiques, mais elle est sujette à changement. Pourtant, par opposition aux approches progressives de la littérature, où l'ensemble de données est enrichie avec de nouveaux gènes (instances) au cours du processus de regroupement, nos approches abordent les cas lorsque de nouvelles fonctionnalités (niveaux d'expression pour de nouveaux points dans le temps) sont ajoutés à la gènes déjà existants dans l'ensemble de données. À notre connaissance, il n'y a pas d'approches dans la littérature qui traitent le problème de la classification dynamique des données d'expression génétique, définis comme ci-dessus. Dans ce contexte, nous avons introduit trois algorithmes de groupement dynamiques que sont capables de gérer de nouveaux niveaux d'expression génique collectés, en partant d'une partition obtenue précédente, sans la nécessité de ré-exécuter l'algorithme à partir de zéro. L'évaluation expérimentale montre que notre méthode est plus rapide et plus précis que l'application de l'algorithme de classification à partir de zéro sur la fonctionnalité étendue ensemble de donnéesThe research direction we are focusing on in the thesis is applying dynamic machine learning models to salve supervised and unsupervised classification problems. We are living in a dynamic environment, where data is continuously changing and the need to obtain a fast and accurate solution to our problems has become a real necessity. The particular problems that we have decided te approach in the thesis are pedestrian recognition (a supervised classification problem) and clustering of gene expression data (an unsupervised classification. problem). The approached problems are representative for the two main types of classification and are very challenging, having a great importance in real life.The first research direction that we approach in the field of dynamic unsupervised classification is the problem of dynamic clustering of gene expression data. Gene expression represents the process by which the information from a gene is converted into functional gene products: proteins or RNA having different roles in the life of a cell. Modern microarray technology is nowadays used to experimentally detect the levels of expressions of thousand of genes, across different conditions and over time. Once the gene expression data has been gathered, the next step is to analyze it and extract useful biological information. One of the most popular algorithms dealing with the analysis of gene expression data is clustering, which involves partitioning a certain data set in groups, where the components of each group are similar to each other. In the case of gene expression data sets, each gene is represented by its expression values (features), at distinct points in time, under the monitored conditions. The process of gene clustering is at the foundation of genomic studies that aim to analyze the functions of genes because it is assumed that genes that are similar in their expression levels are also relatively similar in terms of biological function.The problem that we address within the dynamic unsupervised classification research direction is the dynamic clustering of gene expression data. In our case, the term dynamic indicates that the data set is not static, but it is subject to change. Still, as opposed to the incremental approaches from the literature, where the data set is enriched with new genes (instances) during the clustering process, our approaches tackle the cases when new features (expression levels for new points in time) are added to the genes already existing in the data set. To our best knowledge, there are no approaches in the literature that deal with the problem of dynamic clustering of gene expression data, defined as above. In this context we introduced three dynamic clustering algorithms which are able to handle new collected gene expression levels, by starting from a previous obtained partition, without the need to re-run the algorithm from scratch. Experimental evaluation shows that our method is faster and more accurate than applying the clustering algorithm from scratch on the feature extended data set
8
Salazar, González Fernando. "A machine learning based methodology for anomaly detection in dam behaviour." Doctoral thesis, Universitat Politècnica de Catalunya, 2017. http://hdl.handle.net/10803/405808.
Повний текст джерелаАнотація:
Dam behaviour is difficult to predict with high accuracy. Numerical models for structural calculation solve the equations of continuum mechanics, but are subject to considerable uncertainty as to the characterisation of materials, especially with regard to the foundation. As a result, these models are often incapable to calculate dam behaviour with sufficient precision. Thus, it is difficult to determine whether a given deviation between model results and monitoring data represent a relevant anomaly or incipient failure.
By contrast, there is a tendency towards automatising dam monitoring devices, which allows for increasing the reading frequency and results in a greater amount and variety of data available, such as displacements, leakage, or interstitial pressure, among others.
This increasing volume of dam monitoring data makes it interesting to study the ability of advanced tools to extract useful information from observed variables.
In particular, in the field of Machine Learning (ML), powerful algorithms have been developed to face problems where the amount of data is much larger or the underlying phenomena is much less understood.
In this thesis, the possibilities of machine learning techniques were analysed for application to dam structural analysis based on monitoring data. The typical characteristics of the data sets available in dam safety were taking into account, as regards their nature, quality and size.
A critical literature review was performed, from which the key issues to consider for implementation of these algorithms in dam safety were identified. A comparative study of the accuracy of a set of algorithms for predicting dam behaviour was carried out, considering radial and tangential displacements and leakage flow in a 100-m high dam. The results suggested that the algorithm called ``Boosted Regression Trees'' (BRT) is the most suitable, being more accurate in general, while flexible and relatively easy to implement.
At a later stage, the possibilities of interpretation of the mentioned algorithm were evaluated, to identify the shape and intensity of the association between external variables and the dam response, as well as the effect of time. The tools were applied to the same test case, and allowed more accurate identification of the time effect than the traditional statistical method.
Finally, a methodology for the implementation of predictive models based on BRT for early detection of anomalies was developed and implemented in an interactive tool that provides information on dam behaviour, through a set of selected devices. It allows the user to easily verify whether the actual data for each of these devices are within a pre-defined normal operation interval.El comportamiento estructural de las presas de embalse es difícil de predecir con precisión. Los modelos numéricos para el cálculo estructural resuelven las ecuaciones de la mecánica de medios continuos, pero están sujetos a una gran incertidumbre en cuanto a la caracterización de los materiales, especialmente en lo que respecta a la cimentación. Como consecuencia, frecuentemente estos modelos no son capaces de calcular el comportamiento de las presas con suficiente precisión. Así, es difícil discernir si un estado que se aleja en cierta medida de la normalidad supone o no una situación de riesgo estructural. Por el contrario, muchas de las presas en operación cuentan con un gran número de aparatos de auscultación, que registran la evolución de diversos indicadores como los movimientos, el caudal de filtración, o la presión intersticial, entre otros. Aunque hoy en día hay muchas presas con pocos datos observados, hay una tendencia clara hacia la instalación de un mayor número de aparatos que registran el comportamiento con mayor frecuencia. Como consecuencia, se tiende a disponer de un volumen creciente de datos que reflejan el comportamiento de la presa, lo cual hace interesante estudiar la capacidad de herramientas desarrolladas en otros campos para extraer información útil a partir de variables observadas. En particular, en el ámbito del Aprendizaje Automático (Machine Learning), se han desarrollado algoritmos muy potentes para entender fenómenos cuyo mecanismo es poco conocido, acerca de los cuales se dispone de grandes volúmenes de datos. En la tesis se ha hecho un análisis de las posibilidades de las técnicas más recientes de aprendizaje automático para su aplicación al análisis estructural de presas basado en los datos de auscultación. Para ello se han tenido en cuenta las características habituales de las series de datos disponibles en las presas, en cuanto a su naturaleza, calidad y cantidad. Se ha realizado una revisión crítica de la bibliografía existente, a partir de la cual se han identificado los aspectos clave a tener en cuenta para implementación de estos algoritmos en la seguridad de presas. Se ha realizado un estudio comparativo de la precisión de un conjunto de algoritmos para la predicción del comportamiento de presas considerando desplazamientos radiales, tangenciales y filtraciones. Para ello se han utilizado datos reales de una presa bóveda. Los resultados sugieren que el algoritmo denominado ``Boosted Regression Trees'' (BRTs) es el más adecuado, por ser más preciso en general, además de flexible y relativamente fácil de implementar. En una etapa posterior, se han identificado las posibilidades de interpretación del citado algoritmo para extraer la forma e intensidad de la asociación entre las variables exteriores y la respuesta de la presa, así como el efecto del tiempo. Las herramientas empleadas se han aplicado al mismo caso piloto, y han permitido identificar el efecto del tiempo con más precisión que el método estadístico tradicional. Finalmente, se ha desarrollado una metodología para la aplicación de modelos de predicción basados en BRTs en la detección de anomalías en tiempo real. Esta metodología se ha implementado en una herramienta informática interactiva que ofrece información sobre el comportamiento de la presa, a través de un conjunto de aparatos seleccionados. Permite comprobar a simple vista si los datos reales de cada uno de estos aparatos se encuentran dentro del rango de funcionamiento normal de la presa.
9
Winikoff, Steven M. "Incorporating the simplicity first methodology into a machine learning genetic algorithm." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ39118.pdf.
Повний текст джерела
10
Brun, Yuriy 1981. "Software fault identification via dynamic analysis and machine learning." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/17939.
Повний текст джерелаАнотація:
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (p. 65-67).
I propose a technique that identifies program properties that may indicate errors. The technique generates machine learning models of run-time program properties known to expose faults, and applies these models to program properties of user-written code to classify and rank properties that may lead the user to errors. I evaluate an implementation of the technique, the Fault Invariant Classifier, that demonstrates the efficacy of the error finding technique. The implementation uses dynamic invariant detection to generate program properties. It uses support vector machine and decision tree learning tools to classify those properties. Given a set of properties produced by the program analysis, some of which are indicative of errors, the technique selects a subset of properties that are most likely to reveal an error. The experimental evaluation over 941,000 lines of code, showed that a user must examine only the 2.2 highest-ranked properties for C programs and 1.7 for Java programs to find a fault-revealing property. The technique increases the relevance (the concentration of properties that reveal errors) by a factor of 50 on average for C programs, and 4.8 for Java programs.
by Yuriy Brun.
M.Eng.
Книги з теми "DYNAMIC MACHINE LEARNING METHODOLOGY"
1
Russell, David W. The BOXES Methodology: Black Box Dynamic Control. London: Springer London, 2012.
Знайти повний текст джерела
2
Gultekin, San. Dynamic Machine Learning with Least Square Objectives. [New York, N.Y.?]: [publisher not identified], 2019.
Знайти повний текст джерела
3
Bennaceur, Amel, Reiner Hähnle, and Karl Meinke, eds. Machine Learning for Dynamic Software Analysis: Potentials and Limits. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96562-8.
Повний текст джерела
4
Hinders, Mark K. Intelligent Feature Selection for Machine Learning Using the Dynamic Wavelet Fingerprint. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49395-0.
Повний текст джерела
5
IEEE, International Symposium on Approximate Dynamic Programming and Reinforcement Learning (1st 2007 Honolulu Hawaii). 2007 IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning: Honolulu, HI, 1-5 April 2007. Piscataway, NJ: IEEE, 2007.
Знайти повний текст джерела
6
Kelly, Michael A. A methodology for software cost estimation using machine learning techniques. Monterey, Calif: Naval Postgraduate School, 1993.
Знайти повний текст джерела
7
Maximize the teaching/learning dynamic: A developmental approach for educators. 3rd ed. Denver, Colo: Higher Level, 2013.
Знайти повний текст джерела
8
Slater, Stanley F. Information search style and business performance in dynamic and stable environments: An exploratory study. Cambridge, Mass: Marketing Science Institute, 1997.
Знайти повний текст джерела
9
Ehramikar, Soheila. The enhancement of credit card fraud detection systems using machine learning methodology. Ottawa: National Library of Canada, 2000.
Знайти повний текст джерела
10
IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning (1st 2007 Honolulu, Hawaii). 2007 IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning: Honolulu, HI, 1-5 April 2007. Piscataway, NJ: IEEE, 2007.
Знайти повний текст джерелаЧастини книг з теми "DYNAMIC MACHINE LEARNING METHODOLOGY"
1
Kaur, Manmeet, Krishna Kant Agrawal, and Deepak Arora. "Dynamic Sentiment Analysis Using Multiple Machine Learning Algorithms: A Comparative Knowledge Methodology." In Advances in Data and Information Sciences, 273–86. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8360-0_26.
Повний текст джерела
2
Cammozzo, Alberto, Emanuele Di Buccio, and Federico Neresini. "Monitoring Technoscientific Issues in the News." In ECML PKDD 2020 Workshops, 536–53. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65965-3_37.
Повний текст джерелаАнотація:
AbstractResearch at the intersection between Science and Technology Studies (STS) and Public Communication of Science and Technology (PCST) investigates the role of science in society and how it is publicly perceived. An increasing attention has been paid to coverage of Science and Technology (S&T) issues in newspapers. Because of the availability of a huge amount of digitized news contents, the variety of the issues and their dynamic nature, new opportunities are offered to carry out STS and PCST investigations. The main contribution of this paper is a methodology and a system called TIPS that was co-shaped by sociologists and computer scientists in order to monitor the coverage of S&T issues in the news and to study how they are represented. The methodology relies on machine learning, information retrieval and data analytics approaches which aim at supporting expert users, e.g. sociologists, in the investigation of their research hypotheses.
3
Lee, Sangkyu, and Issam El Naqa. "Machine Learning Methodology." In Machine Learning in Radiation Oncology, 21–39. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18305-3_3.
Повний текст джерела
4
Basuchoudhary, Atin, James T. Bang, and Tinni Sen. "Methodology." In Machine-learning Techniques in Economics, 19–28. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69014-8_3.
Повний текст джерела
5
Dawar, Kshitij, Sanjay Srinivasan, and Mort D. Webster. "Application of Reinforcement Learning for Well Location Optimization." In Springer Proceedings in Earth and Environmental Sciences, 81–110. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19845-8_7.
Повний текст джерелаАнотація:
AbstractThe extensive deployment of sensors in oilfield operation and management has led to the collection of vast amounts of data, which in turn has enabled the use of machine learning models to improve decision-making. One of the prime applications of data-based decision-making is the identification of optimum well locations for hydrocarbon recovery. This task is made difficult by the relative lack of high-fidelity data regarding the subsurface to develop precise models in support of decision-making. Each well placement decision not only affects eventual recovery but also the decisions affecting future wells. Hence, there exists a tradeoff between recovery maximization and information gain. Existing methodologies for placement of wells during the early phases of reservoir development fail to take an abiding view of maximizing reservoir profitability, instead focusing on short-term gains. While improvements in drilling technologies have dramatically lowered the costs of producing hydrocarbon from prospects and resulted in very efficient drilling operations, these advancements have led to sub-optimal and haphazard placement of wells. This can lead to considerable number of unprofitable wells being drilled which, during periods of low oil and gas prices, can be detrimental for a company’s solvency. The goal of the research is to present a methodology that builds machine learning models, integrating geostatistics and reservoir flow dynamics, to determine optimum future well locations for maximizing reservoir recovery. A deep reinforcement learning (DRL) framework has been proposed to address the issue of long-horizon decision-making. The DRL reservoir agent employs intelligent sampling and utilizes a reward framework that is based on geostatistical and flow simulations. The implemented approach provides opportunities to insert expert information while basing well placement decisions on data collected from seismic data and prior well tests. Effects of prior information on the well placement decisions are explored and the developed DRL derived policies are compared to single-stage optimization methods for reservoir development. Under similar reward framework, sequential well placement strategies developed using DRL have been shown to perform better than simultaneous drilling of several wells.
6
Webb, Geoffrey I., Johannes Fürnkranz, Johannes Fürnkranz, Johannes Fürnkranz, Geoffrey Hinton, Claude Sammut, Joerg Sander, et al. "Dynamic Programming." In Encyclopedia of Machine Learning, 298–308. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_237.
Повний текст джерела
7
Webb, Geoffrey I., Johannes Fürnkranz, Johannes Fürnkranz, Johannes Fürnkranz, Geoffrey Hinton, Claude Sammut, Joerg Sander, et al. "Dynamic Systems." In Encyclopedia of Machine Learning, 308. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_239.
Повний текст джерела
8
Hao, Jiangang. "Supervised Machine Learning." In Methodology of Educational Measurement and Assessment, 159–71. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74394-9_9.
Повний текст джерела
9
Wong, Pak Chung. "Unsupervised Machine Learning." In Methodology of Educational Measurement and Assessment, 173–93. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74394-9_10.
Повний текст джерела
10
Webb, Geoffrey I., Johannes Fürnkranz, Johannes Fürnkranz, Johannes Fürnkranz, Geoffrey Hinton, Claude Sammut, Joerg Sander, et al. "Dynamic Bayesian Network." In Encyclopedia of Machine Learning, 298. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_234.
Повний текст джерелаТези доповідей конференцій з теми "DYNAMIC MACHINE LEARNING METHODOLOGY"
1
Cristobo, Leire, Eva Ibarrola, Mark Davis, and Itziar Casado-O'mara. "A Machine Learning Methodology for Dynamic QoX Management in Modern Networks." In 2022 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2022. http://dx.doi.org/10.1109/wcnc51071.2022.9771805.
Повний текст джерела
2
Calabrese, Matteo, Martin Cimmino, Martina Manfrin, Francesca Fiume, Dimos Kapetis, Maura Mengoni, Silvia Ceccacci, et al. "An Event Based Machine Learning Framework for Predictive Maintenance in Industry 4.0." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97917.
Повний текст джерелаАнотація:
Abstract Predictive Maintenance concerns the smart monitoring of machine to avoid possible future failures, since because it is better to intervene before the damage occurs, saving time and money. In this paper, a Predictive Maintenance methodology based on Machine learning approach is presented and it is applied to a real cutting machine, a woodworking machinery in a real industrial group, producing accurate estimations. This kind of strategy is important to deal with maintenance problems given the ever increasing need to reduce downtime and associated costs. The Predictive Maintenance methodology implemented allows dynamical decision rules that have to be considered for maintenance prediction using a combined approach on Azure Machine Learning Studio. The Three models (RF, GBM and XGBM) allowed the accurately predict machine down ever gripped bearing thanks to the pre-processing phases.
3
Alfonso, Carlos Esteban, Frédérique Fournier, and Victor Alcobia. "A Machine Learning Methodology for Rock-Typing Using Relative Permeability Curves." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205989-ms.
Повний текст джерелаАнотація:
Abstract The determination of the petrophysical rock-types often lacks the inclusion of measured multiphase flow properties as the relative permeability curves. This is either the consequence of a limited number of SCAL relative permeability experiments, or due to the difficulty of linking the relative permeability characteristics to standard rock-types stemming from porosity, permeability and capillary pressure. However, as soon as the number of relative permeability curves is significant, they can be processed under the machine learning methodology stated by this paper. The process leads to an automatic definition of relative permeability based rock-types, from a precise and objective characterization of the curve shapes, which would not be achieved with a manual process. It improves the characterization of petrophysical rock-types, prior to their use in static and dynamic modeling. The machine learning approach analyzes the shapes of curves for their automatic classification. It develops a pattern recognition process combining the use of principal component analysis with a non-supervised clustering scheme. Before this, the set of relative permeability curves are pre-processed (normalization with the integration of irreducible water and residual oil saturations for the SCAL relative permeability samples from an imbibition experiment) and integrated under fractional flow curves. Fractional flow curves proved to be an effective way to unify the relative permeability of the two fluid phases, in a unique curve that characterizes the specific poral efficiency displacement of this rock sample. The methodology has been tested in a real data set from a carbonate reservoir having a significant number of relative permeability curves available for the study, in addition to capillary pressure, porosity and permeability data. The results evidenced the successful grouping of the relative permeability samples, according to their fractional flow curves, which allowed the classification of the rocks from poor to best displacement efficiency. This demonstrates the feasibility of the machine learning process for defining automatically rock-types from relative permeability data. The fractional flow rock-types were compared to rock-types obtained from capillary pressure analysis. The results indicated a lack of correspondence between the two series of rock-types, which testifies the additional information brought by the relative permeability data in a rock-typing study. Our results also expose the importance of having good quality SCAL experiments, with an accurate characterization of the saturation end-points, which are used for the normalization of the curves, and a consistent sampling for both capillary pressure and relative permeability measurements.
4
Bonamour, Pierre, Gianni Naccarato, Frederic Champavier, Ammar Mechouche, Nassia Daouayry, and Lucas Macchi. "Use of Machine Learning to Define Optimum HUMS Acquisition Strategy." In Vertical Flight Society 75th Annual Forum & Technology Display. The Vertical Flight Society, 2019. http://dx.doi.org/10.4050/f-0075-2019-14729.
Повний текст джерелаАнотація:
Health and Usage Monitoring Systems (HUMS) measure vibrations levels and compute Vibration Condition Indicators to monitor Helicopters dynamic systems. Modern avionics record continuously a wealth of flight parameters. The influence of flight parameters on Vibration Condition Indicators was assessed using machine learning methods. Machine learning was used to derive Vibration Condition Indicators from flight parameters only. This derivation yielded quantitatively the influence of flight parameters. An illustration of this methodology is presented on main rotor speed. Variable rotor speed contributes to better acoustic performance but is a daunting challenge for HUMS vibration monitoring. Several Vibration Condition Indicators were modelled and their dependency to rotor speed was determined by machine learning parameter weight output. This allowed to optimize the acquisition parameters, the filtering of Vibration Condition Indicators and eventually to classify and choose the most relevant one based on machine learning and extensive replay of fleet data.
5
Orta Aleman, Dante, and Roland Horne. "Well Interference Detection from Long-Term Pressure Data Using Machine Learning and Multiresolution Analysis." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/206354-ms.
Повний текст джерелаАнотація:
Abstract Knowledge of reservoir heterogeneity and connectivity is fundamental for reservoir management. Methods such as interference tests or tracers have been developed to obtain that knowledge from dynamic data. However, detecting well connectivity using interference tests requires long periods of time with a stable reservoir pressure and constant flow-rate conditions. Conversely, the long duration and high frequency of well production data have high value for detecting connectivity if noise, abrupt changes in flow-rate and missing data are dealt with. In this work, a methodology to detect interference from longterm pressure and flow-rate data was developed using multiresolution analysis in combination with machine learning algorithms. The methodology presents high accuracy and robustness to noise while requiring little to no data preprocessing. The methodology builds on previous work using the Maximal Overlap Wavelet Transform (MODWT) to analyze long-term pressure data. The new approach uses the ability of the MODWT to capture, synthesize and discriminate the relevant reservoir response for each individual well at different time scales while still honoring the relevant flow-physics. By first applying the MODWT to the flow rate history, a machine learning algorithm was used to estimate the pressure response of each well as it would be in isolation. Interference can be detected by comparing the output of the machine learning model with the unprocessed pressure data. A set of machine learning, and deep learning algorithms were tested including Kernel Ridge Regression, Lasso Regression and Recurrent Neural Networks. The machine learning models were able to detect interference at different distances even with the presence of high noise and missing data. The results were validated by comparing the machine learning output with the theoretical pressure response of wells in isolation. Additionally, it was proved that applying the MODWT multiresolution analysis to pressure and flow-rate data creates a set of "virtual wells" that still follow the diffusion equation and allow for a simplified analysis. By using production data, the proposed methodology allows for the detection of interference effects without the need of a stabilized pressure field. This allows for a significant cost reduction and no operational overhead because the detection does not require well shut-ins and it can be done regardless of operation opportunities or project objectives. Additionally, the long-term nature of production data can detect connectivity even at long distances even in the presence of noise and incomplete data.
6
JL, Guevara, and Trivedi Japan. "Towards a Machine Learning Based Dynamic Surrogate Modeling and Optimization of Steam Injection Policy in SAGD." In SPE Western Regional Meeting. SPE, 2022. http://dx.doi.org/10.2118/209245-ms.
Повний текст джерелаАнотація:
Abstract This paper presents a methodology for the identification of a dynamic-surrogate model and the optimization of steam injection rates of a multi-well heterogeneous SAGD process. The optimization refers to finding the steam injection rates at every time step (steam injection policy) that will maximize cumulative net present value at the end of the production horizon. The solution methodology consists of identifying one-step prediction non-linear models and then using these models in a recursive scheme to predict the established production horizon. These models are identified offline and then used as a substitute for the reservoir simulation model, considered computationally expensive, in the optimization process. This approach makes use of the reinforcement learning agent-environment interaction: based on the current state St, the agent takes an action At, and the environment transitions into a new state St+1 and offers a scalar reward Rt. Additionally, the well-known genetic algorithm is used for optimization purposes. The approach is applied to a multi-well reservoir simulation model, built using publicly available data that includes data from northern Alberta SAGD operations considering two (2) time step lengths: daily (Case 1) and weekly (Case 2). Furthermore, the performance of the approach is evaluated in terms of: i) Mean Absolute Error (MAE) between the predicted time-series and the true values (effectivity), ii) the effect of randomness of the design of experiments over the MAE (robustness regarding the design of experiments) and iii) changes in the variance of the errors over the prediction time frame (performance as number of time step prediction increases). Results show that for a daily time step (Case 1) the proposed approach was able to predict significantly well the selected output as opposed to Case 2 which exhibit much higher MAE values. Also, there is a small but important effect of the randomness of the design of experiments over the MAE values in both cases. Furthermore, Case 1 showed a significant higher level of robustness over the prediction than Case 2. In particular, the changes in variance of the error in Case 1 was much less that for Case 2.
7
Qian, Chao. "Towards Theoretically Grounded Evolutionary Learning." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/819.
Повний текст джерелаАнотація:
Machine learning tasks are often formulated as complex optimization problems, where the objective function can be non-differentiable, non-continuous, non-unique, inaccurate, dynamic, and have many local optima, making conventional optimization algorithms fail. Evolutionary Algorithms (EAs), inspired by Darwin's theory of evolution, are general-purpose randomized heuristic optimization algorithms, mimicking variational reproduction and natural selection. EAs have yielded encouraging outcomes for solving complex optimization problems (e.g., neural architecture search) in machine learning. However, due to the heuristic nature of EAs, most outcomes to date have been empirical and lack theoretical support, encumbering their acceptance to the general machine learning community. In this paper, I will review the progress towards theoretically grounded evolutionary learning, from the aspects of analysis methodology, theoretical perspectives and learning algorithms. Due to space limit, I will include a few representative examples and highlight our contributions. I will also discuss some future challenges.
8
Chen, Peng, Changhong Hu, and Zhiqiang Hu. "Software-in-the-Loop Combined Machine Learning for Dynamic Responses Analysis of Floating Offshore Wind Turbines." In ASME 2021 40th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/omae2021-65524.
Повний текст джерелаАнотація:
Abstract Artificial intelligence (AI) brings a new solution to overcome the challenges of Floating offshore wind turbines (FOWTs) to better predict the dynamic responses with intelligent strategies. A new AI-based software-in-the-loop method, named SADA is introduced in this paper for the prediction of dynamic responses of FOWTs, which is proposed based on an in-house programme DARwind. DARwind is a coupled aero-hydro-servo-elastic in-house program for FOWTs, and a reinforcement learning method with exhaust algorithm and deep deterministic policy gradient (DDPG) are embedded in DARwind as an AI module. Firstly, the methodology is introduced with the selection of Key Disciplinary Parameters (KDPs). Secondly, Brute-force Method and DDPG algorithms are adopted to changes the KDPs’ values according to the feedback of 6DOF motions of Hywind Spar-type platform through comparing the DARwind simulation results and those of basin experimental data. Therefore, many other dynamic responses that cannot be measured in basin experiment can be predicted in good accuracy with SADA method. Finally, the case study of SADA method was conducted and the results demonstrated that the mean values of the platform’s motions can be predicted with higher accuracy. This proposed SADA method takes advantage of numerical-experimental method, basin experimental data and the machine learning technology, which brings a new and promising solution for overcoming the handicap impeding direct use of conventional basin experimental way to analyze FOWT’s dynamic responses during the design phase.
9
Marko, Kenneth. "Machine Learning and Model Based Reasoning for Prognostics of Complex Systems." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-81625.
Повний текст джерелаАнотація:
Model based reasoning (MBR) has been shown to be an effective means of providing condition based maintenance for many high-value assets for which accurate first principle models have been developed. Yet, many low-cost complex computer controlled systems are mass-produced without the concurrent provision of precise physics based models. We wish to utilize new developments in machine learning coupled with model based reasoning methods to address this deficiency. In particular, we shall demonstrate that for an important class of these systems, the extremely large number of mass produced, complex engine systems which power vehicles and small power generation plants, effective means of providing MBR for condition based maintenance exists. It will be recognized that the methodology also has much broader applicability. We will show that a class of dynamic neural networks can be used to provide high-fidelity models of these complex systems that permit an analysis of differences between predicted normal behavior and actual plant behavior to be analyzed to detect deviations from nominal behavior which will be shown to be valuable in estimating time-to-failure for such systems. The realization of this capability is dependent upon the development of extremely efficient and powerful training algorithms for these dynamics neural networks. While many simple training schemes have been in use for many years, they generally fail to provide the needed model accuracy when they are applied to training the relatively “large” multi-layered dynamic networks that are needed to precisely mimic plant behavior over all operating conditions. Our approach has several advantages over these simpler, but less effective methods. Three major improvements are the rate at which learning proceeds, the provision of a means to optimize the learning rate through-out the process, and the dramatic improvements observed in learning in the final stages of training when the error feedback from training examples are extremely small and the associated error covariance matrices almost vanish. We shall demonstrate with data drawn from production vehicles, that for several important problems in analyzing system performance in these vehicles, sufficient model fidelity can be attained to meet the requirements on detection efficiency, false alarm immunity and alarm response time which are required for effective diagnostics and prognostics. Finally we shall discuss the manner in which the deviations are analyzed to not only identify that a failure has been detected but also the means by which the probable root cause may be isolated.
10
Wei, Hangchuan, Yota Adilenido, and Richard Beckett. "Environmental-driven Massing Based on Machine learning." In Design Computation Input/Output 2022. Design Computation, 2022. http://dx.doi.org/10.47330/dcio.2022.eqad1156.
Повний текст джерелаАнотація:
In recent years, machine learning (ML) has received significant attention in the field of architectural design. This paper proposes a methodology for integrating ML with computational design to generate building massing based on environment, in this way, gives an outlook on the application of ML in architecture. In the early stages of building design, a great deal of effort is often spent on specifying and designing building massing. In this process, the assessment of the building wind performance plays an important role. Compared to professional computational fluid dynamics (CFD) software, plug-ins based on rhino and grasshopper, such like Butterfly and Eddy3D, can well integrated into computational design process. But even then, these plug-ins are still limited because a lot of computing power and time are required to run the program. This article provides an overview of a generative framework embedded with a ML approach to apply CFD in building design, finally results on a building massing with a balanced wind environment at the early stage of architectural design. This framework innovates the existing CFD simulation in following aspects: 1) ML-based simulation is timesaving, 2) this advantage allows the use of exhaustive enumeration to obtain the optimal solution, 3) this framework provides a good interface with computational design process with images as a medium, 4) therefore it is more flexible and operational. This framework aims to provide an approach to achieve faster and better massing design. To reach this objective, there are three main steps: 1) firstly, a generative adversarial network (GAN) model is trained to get wind simulation results from the input site, 2) then, the possible boundaries of massing in different height are generated for exhaustive enumeration, 3) afterwards, run again the GAN wind simulation for the possible boundaries, 4) and finally an assessment method is put forward to obtain the ideal result for the site.
Звіти організацій з теми "DYNAMIC MACHINE LEARNING METHODOLOGY"
1
Steinfeld, Aaron, Rachael Bennett, Kyle Cunningham, Matt Lahut, Pablo-Alejandro Quinones, Django Wexler, Dan Siewiorek, Paul Cohen, Julie Fitzgerald, and Othar Hansson. The RADAR Test Methodology: Evaluating a Multi-Task Machine Learning System with Humans in the Loop. Fort Belvoir, VA: Defense Technical Information Center, October 2006. http://dx.doi.org/10.21236/ada457300.
Повний текст джерела
2
Zhang, Ruirui, Shan Xue, and Leslie D. Burns. Investigation of Micro-blogging marketing strategy of Fashion brand: via big data and machine learning methodology. Ames: Iowa State University, Digital Repository, November 2015. http://dx.doi.org/10.31274/itaa_proceedings-180814-153.
Повний текст джерела
3
Ao, Tommy, Brendan Donohoe, Carianne Martinez, Marcus Knudson, Dane Morgan, Mark Rodriguez, and James Lane. LDRD 226360 Final Project Report: Simulated X-ray Diffraction and Machine Learning for Optimizing Dynamic Experiment Analysis. Office of Scientific and Technical Information (OSTI), October 2022. http://dx.doi.org/10.2172/1891594.
Повний текст джерела
4
Gonzalez Pibernat, Gabriel, and Miguel Mascaró Portells. Dynamic structure of single-layer neural networks. Fundación Avanza, May 2023. http://dx.doi.org/10.60096/fundacionavanza/2392022.
Повний текст джерелаАнотація:
This article examines the practical applications of single hidden layer neural networks in machine learning and artificial intelligence. They have been used in diverse fields, such as finance, medicine, and autonomous vehicles, due to their simplicit
5
Rossen, Lauren, Brady Hamilton E., Joyce Abma, Elizabeth C.W., Vladislav Beresovsky, Andriana Resendez, Anjani Chandra, and Joyce Martin. Updated Methodology to Estimate Overall and Unintended Pregnancy Rates in the United States. National Center for Health Statistics (U.S.), April 2023. http://dx.doi.org/10.15620/cdc:124395.
Повний текст джерелаАнотація:
This report describes an updated methodology to estimate overall and unintended pregnancy rates for the United States from 2010–2019, and examines differences by demographic factors. Machine learning models were used to impute missing information on induced abortions, and data integration methods were used to combine estimates of abortions, live births, and pregnancy losses to produce estimates of the total numbers of pregnancies occurring from 2010–2019 and corresponding pregnancy rates, along with outcomes related to unintended pregnancy.
6
Rossen, Lauren, Brady Hamilton E., Joyce Abma, Elizabeth C.W., Vladislav Beresovsky, Adriana Resendez, Anjani Chandra, and Joyce Martin. Updated Methodology to Estimate Overall and Unintended Pregnancy Rates in the United States. National Center for Health Statistics (U.S.), April 2023. http://dx.doi.org/10.15620/cdc:124369.
Повний текст джерелаАнотація:
This report describes an updated methodology to estimate overall and unintended pregnancy rates for the United States from 2010–2019, and examines differences by demographic factors. Machine learning models were used to impute missing information on induced abortions, and data integration methods were used to combine estimates of abortions, live births, and pregnancy losses to produce estimates of the total numbers of pregnancies occurring from 2010–2019 and corresponding pregnancy rates, along with outcomes related to unintended pregnancy.
7
Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
Повний текст джерелаАнотація:
The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
8
Hovakimyan, Naira, Hunmin Kim, Wenbin Wan, and Chuyuan Tao. Safe Operation of Connected Vehicles in Complex and Unforeseen Environments. Illinois Center for Transportation, August 2022. http://dx.doi.org/10.36501/0197-9191/22-016.
Повний текст джерелаАнотація:
Autonomous vehicles (AVs) have a great potential to transform the way we live and work, significantly reducing traffic accidents and harmful emissions on the one hand and enhancing travel efficiency and fuel economy on the other. Nevertheless, the safe and efficient control of AVs is still challenging because AVs operate in dynamic environments with unforeseen challenges. This project aimed to advance the state-of-the-art by designing a proactive/reactive adaptation and learning architecture for connected vehicles, unifying techniques in spatiotemporal data fusion, machine learning, and robust adaptive control. By leveraging data shared over a cloud network available to all entities, vehicles proactively adapted to new environments on the proactive level, thus coping with large-scale environmental changes. On the reactive level, control-barrier-function-based robust adaptive control with machine learning improved the performance around nominal models, providing performance and control certificates. The proposed research shaped a robust foundation for autonomous driving on cloud-connected highways of the future.
9
Kramarenko, T. H., O. S. Pylypenko, and O. Yu Serdiuk. Digital technologies in specialized mathematics education: application of GeoGebra in Stereometry teaching. [б. в.], 2021. http://dx.doi.org/10.31812/123456789/4534.
Повний текст джерелаАнотація:
The purpose of the paper is to improve methodology of teaching Mathematics via the use of digital technologies. The task of the paper is to identify the issues that require a theoretical and experimental solution. The objective of the paper is the educational process in the higher education institution, the subject of the paper is modern ICT. The result of the study is the learning tools of pedagogically considered and adequate bending of conventional and modern learning environment implemented into the educational process. The possibilities of using cloud technologies and Dynamic Mathematics system GeoGebra in the educational process through Stereometry specialized training have been revealed. The use of GeoGebra Dynamic Mathematics in Stereometry teaching will favourably influence the formation of students’ STEM competencies. In order to encourage Mathematics and Computer Science teachers to implement effectively the elements of STEM education, it is suggested that cloud-based learning tools such as GeoGebra be used in the teaching process.
10
Pylypenko, Olha S., Tetiana H. Kramarenko, and Ivan O. Muzyka. Application of GeoGebra in Stereometry teaching. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3898.
Повний текст джерелаАнотація:
The purpose of the paper is to improve methodology of teaching Mathematics via the use of cloud technology. The task of the paper is to identify the issues that require a theoretical and experimental solution. The objective of the paper is the educational process in the higher education institution, the subject of the paper is modern ICT. The result of the study is the learning tools of pedagogically considered and adequate bending of conventional and modern learning environment implemented into the educational process. The possibilities of using cloud technologies and Dynamic Mathematics system GeoGebra in the educational process through Stereometry specialized training have been revealed. The use of GeoGebra Dynamic Mathematics in Stereometry teaching will favourably influence the formation of students’ STEM competencies. In order to encourage Mathematics and Computer Science teachers to implement effectively the elements of STEM education, it is suggested that cloud-based learning tools such as GeoGebra be used in the teaching process.